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Causal Inference Phd Internship Jobs (NOW HIRING)

Alembic

Research Engineer - Causal AI

San Francisco, CA · On-site

$200K - $250K/yr

Build production systems for causal inference that maintain statistical rigor at enterprise scale ... MS or PhD with significant applied research experience * Background in econometrics, statistics, or ...

YO AI Labs

Data Scientist

Glendale, CA · On-site

... PhD + 3 years of relevant experience with an emphasis on experimentation or causal inference. * Experience with ETL and data engineering: data extraction, transformation, integration, and quality ...

YO AI Labs

Data Scientist

Glendale, CA · On-site

... PhD + 3 years of relevant experience with an emphasis on experimentation or causal inference. * Experience with ETL and data engineering: data extraction, transformation, integration, and quality ...

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How much do causal inference phd internship jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for causal inference phd internship in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What is a causal inference PhD internship?

A Causal Inference PhD Internship is a specialized research position for doctoral students focused on causal inference, which involves determining cause-and-effect relationships from data. Interns typically work with large datasets, advanced statistical models, and machine learning techniques to answer questions about how variables influence one another. These internships are often offered by tech companies, research labs, or policy organizations and provide hands-on experience in designing experiments, analyzing observational data, and developing new methodologies. The goal is to bridge academic research with real-world applications, contributing to projects that require rigorous causal analysis.

What types of projects does a causal inference PhD intern typically work on during their internship?

Causal Inference PhD interns often engage in projects that involve designing and analyzing experiments or observational studies to draw valid conclusions about cause-and-effect relationships. These projects might include developing statistical models, collaborating with data scientists and product teams, and presenting findings to inform business or policy decisions. Interns usually have the opportunity to work with large-scale, real-world data, and are encouraged to publish or present their work at conferences, supporting both professional growth and academic development.

What are the key skills and qualifications needed to thrive as a causal inference PhD intern, and why are they important?

To thrive as a Causal Inference PhD Intern, you need a strong background in statistics, econometrics, and causal inference methods, often supported by advanced graduate studies in a related field. Familiarity with statistical programming languages such as R or Python, and experience using data analysis tools and frameworks like Stata or TensorFlow Probability, are typically required. Excellent problem-solving abilities, critical thinking, and the ability to communicate complex concepts clearly help you stand out in this role. These skills and qualities are crucial for designing robust experiments, drawing reliable conclusions, and effectively collaborating with interdisciplinary research teams.

What is the difference between Causal Inference Phd Internship vs Data Scientist Internship?

AspectCausal Inference Phd InternshipData Scientist Internship
Required CredentialsPhD in statistics, economics, or related fieldBachelor's or Master's in CS, statistics, or related field
Work EnvironmentResearch-focused, academic or industry research teamsData analysis, modeling, and business insights
Employer & Industry UsageResearch institutions, tech companies, financeTech firms, startups, finance, healthcare
Search & Comparison IntentFocus on causal inference research rolesBroader data analysis roles

While a Causal Inference Phd Internship emphasizes research in causal analysis with advanced credentials, a Data Scientist Internship covers broader data analysis skills suitable for various industries. Both roles involve working with data, but their focus, required background, and career paths differ significantly.

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Causal Inference Phd Internship jobs near you
Infographic showing various Causal Inference Phd Internship job openings in the United States as of August 2026, with employment types broken down into 10% Internship, 56% Full Time, 32% Part Time, 1% Temporary, and 1% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.

Machine Learning Engineer, Causal Inference, Level 5

Jobtailor

California, MO • On-site

$150 - $210/hr

Other

Posted 8 days ago

Job description

  • Design and build models that quantify causal impact, optimize decision‑making, and drive value for users, advertisers, and the business
  • Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data
  • Design, analyze, and interpret A/B tests and quasi‑experiments; collaborate closely with product and engineering partners to shape experimentation strategies
  • Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability
  • Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
  • Contribute to rapid iteration cycles while ensuring methodological rigor
Requirements
  • Bachelor’s degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience
  • 5+ years of post‑Bachelor’s experience in machine learning, with hands‑on experience in causal inference or experimentation; or Master’s degree in a technical field + 4+ years of post‑grad machine learning experience; or PhD in a relevant technical field + 2 years of post‑grad machine learning experience
  • Demonstrated experience building models to support product decision‑making and policy evaluation through causal techniques
  • Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems
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